A Higher Order Fuzzy Logic Model with Genetic Algorithm Used to Predict the Rice Production in India

Abstract

Forecasting paddy production is considered as a difficult problem in the real world due to in deterministic behavior of the nature. Specifically, rice production is forecasted for a leading year for overall planning of the crop, utilization of the agricultural resources and the rice production management. Likewise, the key challenge of the forecasting rice production is to create a realistic model that can able to handle the critical time series data and forecast with minor error. Prognostication of the Future data is highly correlated with the time series data set. If the accuracy of your prediction is more appropriate, then the value of the forecast will improve as well. This paper represents a new technique depends on Higher Order Fuzzy Logical Relationship. Here, Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) are used to estimate the errors of predicted data. historical data relating to the rice production of 1981 to 2003 is used as secondary data and the error of the predicted data is further reduced using different soft computing technique.